Medical imaging has become the bellwether application for artificial intelligence (AI) technologies in healthcare. From deep learning and machine learning approaches, to cognitive computing, to even natural language processing, several AI approaches are now being incorporated in the field of radiology. Apart from academic research groups, almost all major manufacturers and vendors in the medical imaging space have or are developing initiatives to bring automation, augmentation or acceleration to medical imaging, cognitive computing for imaging informatics applications and even intelligent machines. There are several startups as well which continue to advance the development of solutions.
In the medical imaging workflow, from ordering of imaging studies all the way up to follow-up post imaging, artificial intelligence could play a role. Of course, current efforts are mostly concentrated in analyzing the medical images. These solutions have been deployed on premises, but there is a gradual adoption of cloud technologies too. As applications evolve and are developed further, AI is moving to the edge, and might also become embedded in imaging equipment as the next frontier. However, at the global level, countries look up to AI to address very different and systemic challenges – while some such as the United States require higher productivity and standardization, the United Kingdom needs to address shortages and higher wait time, whereas others such as China need it to build access and expertize to improve diagnosis rates. This has naturally resulted in a continuous growth in the number of startups emerging in this field, globally. Funding too, has flowed in to support the momentum. Crucial regulatory milestones have been achieved, but many more are likely to be reached as well.
This study is a focused analysis to highlight all of the companies active in this space, and to analyze their solutions to get a sense of the trends in the industry. Aptly called the Companies-to-Action, a comprehensive analysis of ~100 AI companies currently offering medical image analysis solutions provides an in-depth insight in to several key questions, as outlined below. Competitive landscape, evolving partnerships, regional analysis to identify conducive factors for development of AI solutions, funding trends for the 80+ startups, a landscape assessment, and finally, a list of top ten predictions for the coming 5 years are covered in this study. It also highlights a curated list of companies along with our perspective on their uniqueness, potential opportunities, and threats.

Key Issues Addressed

Who are the companies that have set out to develop and commercialize artificial intelligence-based solutions for medical image analysis?

Which modalities, organs and disease areas have vendors focused on during the inception phase 2011-2018?

What kind of competitive and partnership dynamics are taking place across the vendor, provider and investor ecosystems?

What are some unique approaches and use cases addressed by some of the most innovative companies vested in AI-based medical image analysis?

How do various regions fare in the adoption of, and investment in AI based technologies for medical image analysis?